Evaluating five different loci (<i>rbc</i>L, <i>rpo</i>B, <i>rpo</i>C1, <i>mat</i>K, and ITS) for DNA barcoding of Indian orchids
Bibliographic record
Abstract
Orchidaceae, one of the largest families of angiosperms, is represented in India by 1600 species distributed in diverse habitats. Orchids are in high demand owing to their beautiful flowers and therapeutic properties. Overexploitation and habitat destruction have made many orchid species endangered. In the absence of effective identification methods, illicit trade of orchids continues unabated. Considering DNA barcoding as a potential identification tool, species discrimination capability of five loci, ITS, matK, rbcL, rpoB, and rpoC1, was tested in 393 accessions of 94 Indian orchid species belonging to 47 genera, including one listed in Appendix I of CITES and 26 medicinal species. ITS provided the highest species discrimination rate of 94.9%. While, among the chloroplast loci, matK provided the highest species discrimination rate of 85.7%. None of the tested loci individually discriminated 100% of the species. Therefore, multi-locus combinations of up to five loci were tested for their species resolution capability. Among two-locus combinations, the maximum species resolution (86.7%) was provided by ITS+matK. ITS and matK sequences of the medicinal orchids were species specific, thus providing unique molecular identification tags for their identification and detection. These observations emphasize the need for the inclusion of ITS in the core barcode for plants, whenever required and available.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".